(1) Technical Field
The present invention relates to techniques for processing imagery. More specifically, the present invention relates to a technique for processing imagery by transforming optical flow maps into optical flow histograms and utilizing the histograms to determine positions and widths of objects in a scene.
(2) Description of Related Art
In the automotive market, the presence of active and passive safety systems is a critical factor in a buyer's acceptance of new vehicles released into the market. In addition, regulatory requirements prescribe a minimum standard for safety in the vehicle. Both factors have driven the spread of new technologies for use in automobile safety, such as pedestrian and vehicle detection. Future regulatory requirements may demand automobiles to actively avoid or mitigate collisions with vehicles and pedestrians, which will necessitate the development of pre-crash sensing systems.
In order for pre-crash sensing systems to be effective, they need to segment the objects in a scene being monitored in order to predict potential collisions with a vehicle (i.e., host). Object segmentations created using existing optical flow methods are often noisy, inconsistent, and sparsely sampled, particularly if a host platform is moving.
Previously, optical flow fields have been used for moving object segmentation. A recent approach modeled the road in front of the host vehicle as a planar surface; estimated the ego-motion using some simple assumptions; compensated the ego-motion using this simple model; and then detected the foreground objects.
Unfortunately, the optical flow field method is computationally expensive and cannot currently be implemented in real-time without using specialized hardware. Another drawback is that this method is not robust against violations of the planar road model constraint.
Another moving object segmentation approach involves heuristic attempts to compensate for motion between two contiguous frames by first compensating for pitch and then yaw motion. Akin to video stabilization, compensating for pitch and yaw motion is designed to eliminate background flow, thus exposing the foreground objects with simple differencing. A drawback is that this approach is not very robust and fails when the motion vectors are not small.
Yet another approach for moving object segmentation involves using epi-polar constraints for segmenting objects. This approach is not feasible in real-time and involves computation of the fundamental matrix which is not stable for the slow motions that are typically observed in optical flow.
Thus, what is needed is a system and method for processing imagery, wherein the system and method is not limited to a planar road model constraint, is more robust, is adaptable to larger motion vectors, and is computationally efficient enough to be performed in real-time.